A New Weibull Class of Distributions: Theory, Characterizations and Applications

Yousof, Haitham M.; Majumder, Mahbubul; Jahanshahi, Seyed Mahdi Amir; Ali, M. Masoom; Hamedani, G. G.

doi:10.29252/jsri.15.1.45

Cited by 48 publications

(30 citation statements)

References 32 publications

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“…Based on [10] and using (1.1), then the CDF of the Weibull generalized log-logistic (WGLL) model is defined by…”

Section: Introductionmentioning

confidence: 99%

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Extended Weibull log-logistic distribution

Abouelmagd¹,

Hamed²,

Almamy³

et al. 2019

J. Nonlinear Sci. Appl.

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A new distribution called the Weibull Generalized log logistic distribution is introduced along with a simple physical motivation. Several of its statistical properties are derived. Three applications are provided to illustrate the importance of the new distribution. The new distribution is shown to be better that other important competitive models via three applications. The method of maximum likelihood is used to estimate the unknown parameters.

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“…Based on [10] and using (1.1), then the CDF of the Weibull generalized log-logistic (WGLL) model is defined by…”

Section: Introductionmentioning

confidence: 99%

“…By means of two applications, it is noted that the WGLL model provides better fits than nine competitive models. Following [10], the PDF (1.3) of the WGLL model can be expressed as…”

Section: Introductionmentioning

confidence: 99%

Extended Weibull log-logistic distribution

Abouelmagd¹,

Hamed²,

Almamy³

et al. 2019

J. Nonlinear Sci. Appl.

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“…These include: The Transmuted Weibull lomax distribution by Afify et al, 1 kumaraswamy Marshal-olkin family by Afify et al, 2 Lomax generator by Cordeiro et al, 3 the weibull exponential by Oguntunde et al, 4 kumaraswamy-pareto by Bourguignon et al, 5 weibull-G family by Bourguignon et al, 6 the weibull-dagum distribution by Tahir et al, 7 the generalized transmuted-G family of distributions by Nofal et al, 8 among others. Recently, a lot of extensions of distributions have been proposed and studied based on the Weibull-G family of distributions by Bourguignon et al, 6 Among them is the Tahir, Merovci, Afify, Yousof, and Oguntunde et al [9][10][11][12]4 to mention but few.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

BBIJ

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In this paper, we introduced a four-parameter probability model called Weibull-Inverse Lomax distribution with decreasing, increasing and bathtub hazard rate function. The WIL distribution density function is J-shaped, positively skewed, and J-shaped in reverse. Some of the mentioned distribution's statistical characteristics are provided including moments, order statistics, entropy, mean, variance, moment generating function, and quantile function. The method of maximum likelihood estimation was used to estimate the parameters of the model. The distribution's importance is proved by its implementation to the bladder cancer data set. Goodness-of-fit of this distribution by various techniques demonstrates that the WIL distribution is empirically better for lifetime application.

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“…These generalized distributions give more flexibility by adding one or more parameters to the baseline model. Many classes can be cited such as the Marshall-Olkin-G family by Marshall and Olkin [25], transmuted exponentiated generalized-G family by Yousof et al [34], Burr X-G by Yousof et al [35], type I half-logistic family by Cordeiro et al [12], Zografos-Balakrishnan odd log-logistic family of distributions by Cordeiro et al [13], a new generalized two-sided family of distributions by Korkmaz and Genç [22], generalized odd log-logistic family by Cordeiro et al [10], odd-Burr generalized family by Alizadeh et al [4], beta Weibull G by Yousof et al [36], exponentiated generalized-G Poisson family by Aryal and Yousof [8], type I general exponential class by Hamedani et al [20] and beta transmuted-H by Afify et al [2] among others.…”

Section: Introductionmentioning

confidence: 99%

The Exponential Lindley Odd Log-Logistic-G Family: Properties, Characterizations and Applications

Korkmaz¹,

Yousof²,

Hamedani³

2018

JSTA

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A new family of distributions called the exponential Lindley odd log-logistic G family is introduced and studied. The new generator generalizes three newly defined G families and also defines two new G families. We provide some mathematical properties of the new family. Characterizations based on truncated moments as well as in terms of the hazard function are presented. The maximum likelihood is used for estimating the model parameters. We assess the performance of the maximum likelihood estimators in terms of biases and mean squared errors by means of a simulation study. Finally, the usefulness of the family is illustrated by means of three real data sets. The new model provides consistently better fits than other competitive models for these data sets.

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A New Weibull Class of Distributions: Theory, Characterizations and Applications

Cited by 48 publications

References 32 publications

Extended Weibull log-logistic distribution

Extended Weibull log-logistic distribution

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The Exponential Lindley Odd Log-Logistic-G Family: Properties, Characterizations and Applications

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